In this example the zipcodes_wake vector data and the elev_state_500m raster data from the North Carolina
sample dataset (original raster and vector data) are used to calculate average elevation for every zone.
The important part of the process is the Graphical Modeler, namely its possibilities of process
automation.
Theworkflowshownasaseriesofcommands
In the command console the procedure looks as follows:
# input data import
r.import input=elev_state_500m.tif output=elevation
v.import input=zipcodes_wake.shp output=zipcodes_wake
# computation region settings
g.region vector=zipcodes_wake
# raster statistics (average values), upload to vector map table calculation
v.rast.stats -c map=zipcodes_wake raster=elevation column_prefix=rst method=average
# univariate statistics on selected table column for zipcode map calculation
v.db.univar map=zipcodes_wake column=rst_average
# conversion from vector to raster layer (due to result presentation)
v.to.rast input=zipcodes_wake output=zipcodes_avg use=attr attribute_column=rst_average
# display settings
r.colors -e map=zipcodes_avg color=bgyr
d.mon start=wx0 bgcolor=white
d.barscale style=arrow_ends color=black bgcolor=white fontsize=10
d.rast map=zipcodes_avg bgcolor=white
d.vect map=zipcodes_wake type=boundary color=black
d.northarrow style=1a at=85.0,15.0 color=black fill_color=black width=0 fontsize=10
d.legend raster=zipcodes_avg lines=50 thin=5 labelnum=5 color=black fontsize=10
DefiningtheworkflowintheGraphicalModeler
To start performing above steps as an automatic process with the Graphical Modeler press the icon or
type g.gui.gmodeler. The simplest way of inserting elements is by adding the complete GRASS command to
the Command field in the GRASS command dialog (see figure below). With full text search one can do
faster module hunting. Next, the label and the command can be added. In case that only a module name is
inserted, after pressing the Enter button, the module dialog window is displayed and it is possible to
set all of the usual module options (parameters and flags).
Figure:DialogforaddingGRASScommandstomodel.Managingmodelparameters
All used modules can be parameterized in the model. That causes launching the dialog with input options
for model after the model is run. In this example, input layers (zipcodes_wake vector map and
elev_state_500m raster map) are parameterized. Parameterized elements show their diagram border slightly
thicker than those of unparameterized elements.
Figure:Modelparametersettings.
The final model, the list of all model items, and the Script editor window with Save and Run option are
shown in the figures below.
Figure:Amodeltoperformaveragestatisticsforzipcodezones.Figure:ItemswithScripteditorwindow.
For convenience, this model for the Graphical Modeler is also available for download here.
The model is run by clicking the Run button . When all inputs are set, the results can be displayed as
shown in the next Figure:
Figure:AverageelevationforZIPcodesusingNorthCarolinasampledatasetasanautomaticcalculationperformedbyGraphicalModeler.Managingmodelproperties
When the user wants to run the model again with the same data or the same names, it is necessary to use
--overwrite option. It will cause maps with identical names to be overwritten. Instead of setting it for
every module separately it is handy to change the Model Property settings globally. This dialog includes
also metadata settings, where model name, model description and author(s) of the model can be specified.
Figure:Modelproperties.Definingvariables
Another useful trick is the possibility to set variables. Their content can be used as a substitute for
other items. Value of variables can be values such as raster or vector data, integer, float, string value
or they may constitute some region, mapset, file or direction data type. Then it is not necessary to set
any parameters for input data. The dialog with variable settings is automatically displayed after the
model is run. So, instead of model parameters (e.g. r.import a v.import, see the Figure Runmodeldialog
above) there are Variables.
Figure:Modelwithvariableinputs.
The key point is the usage of % before the substituting variable and settings in the Variables dialog.
For example, in the case of a model variable raster that points to an input file path and which value is
required to be used as one of inputs for a particular model, it should be specified in the Variables
dialog with its respective name (raster), data type, default value and description. Then it should be set
in the module dialog as input called %raster.
Figure:Exampleofrasterfilevariablesettings.Figure:Exampleofrasterfilevariableusage.Savingthemodelfile
Finally, the model settings can be stored as a GRASS GIS Model file with *.gxm extension. The advantage
is that it can be shared as a reusable workflow that may be run also by other users with different data.
For example, this model can later be used to calculate the average precipitation for every administrative
region in Slovakia using the precip raster data from Slovakia precipitation dataset and administration
boundaries of Slovakia from Slovak Geoportal (only with a few clicks).
Handlingintermediatedata
There can be some data in a model that did not exist before the process and that it is not worth it to
maintain after the process executes. They can be described as being Intermediate by single clicking using
the right mouse button, see figure below. All such data should be deleted following model completion. The
boundary of intermediate component is dotted line.
Figure:Usageanddefinitionofintermediatedatainmodel.UsingtheScripteditor
By using the Script editor in the Graphical Modeler, the user can add Python code and then run it with
Run button or just save it as a Python script *.py. The result is shown in the Figure below:
Figure:ScripteditorinthewxGUIGraphicalModeler. The second option in the Scripttype combobox
exports a PyWPS script instead of a basic Python one. A PyWPS process based on the model will be
generated then; for the PyWPS script, the Run button is disabled as users are expected to include this
script in their web processing service and not to run it on itself.
Figure:ScripteditorinthewxGUIGraphicalModeler-settoPyWPS. The third option in the Scripttype
combobox exports an actinia process chain (non-parameterized model) or an actinia template (parameterized
model). An actinia JSON based on the model will be generated then; as for the PyWPS script, the Run
button is disabled as users are expected to include this JSON in their web processing service and not to
run it on itself.
Figure:ScripteditorinthewxGUIGraphicalModeler-settoactinia.
By default GRASS script package API is used (grass.script.core.run_command()). This can be changed in the
settings. Alternatively also PyGRASS API is supported (grass.pygrass.modules.Module).
Definingloops
In the example below the MODIS MOD13Q1 (NDVI) satellite data products are used in a loop. The original
data are stored as coded integer values that need to be multiplied by the value 0.0001 to represent real
ndvivalues. Moreover, GRASS GIS provides a predefined color table called ndvi to represent ndvidata.
In this case it is not necessary to work with every image separately.
The Graphical Modeler is an appropriate tool to process data in an effective way using loop and variables
(%map for a particular MODIS image in mapset and %ndvi for original data name suffix). After the loop
component is added to model, it is necessary to define series of maps with required settings of map type,
mapset, etc.
Figure:MODISdatarepresentationinGRASSGISafterGraphicalModelerusage.
When the model is supplemented by all of modules, these modules should be ticked in the boxes of loop
dialog. The final model and its results are shown below.
Figure:Modelwithloop.Figure:MODISdatarepresentationinGRASSGISafterGraphicalModelerusage.
The steps to enter in the command console of the Graphical Modeler would be as follows:
# note that the white space usage differs from the standard command line usage
# rename original image with preselected suffix
g.rename raster = %map,%map.%ndvi
# convert integer values
r.mapcalc expression = %map = %map.%ndvi * 0.0001
# set color table appropriate for nvdi data
r.colors = map = %map color = ndvi
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